The problem of production scheduling is sufficiently complex to negate
the use of many conventional optimising techniques as there are
numerous and diverse factors that must be considered when trying to
produce an optimal schedule. These factors include: grade distribution,
orebody geometry, production rate capacity, processing constraints and
market price of the minerals amongst a variety of others. In addition it
may be observed that many of these factors incorporate a degree of
uncertainty and any schedule produced must inevitably consider risk as a
primary consideration. The very nature of the problem has limited the use
of conventional optimisers. Genetic algorithms use methods based upon evolution and natural
selection to produce `optimal' solutions to complex system problems.
They are extremely flexible and robust in their nature and it is these
characteristics that are important in this particular problem domain. A
particular strength of the genetic optimisation approach is the ability to
handle the multiple and non-linear constraints that are typically
encountered in mine scheduling problems. This paper concentrates on the development of a prototype application
of a genetic algorithm to the problem of scheduling the output from
multiple production points in an underground mining operation. In
particular it.describes a system (GO-SCHED) that can be customised to
handle a wide variety of underground scheduling problems. GO-SCHED,
a Windows-based system, can interface with a variety of databases and
spreadsheets for data input and output and provides a simple intuitive
approach to the solution of the scheduling problem.